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Visual Quality Assessment by Machine LearningSummary and Remarks for Future Research

Visual Quality Assessment by Machine Learning: Summary and Remarks for Future Research [There has been increasing interest in visual quality assessment (VQA) during recent years. Of all these VQA methods, machine learning (ML) based ones became more and more popular. In this book, ML-based VQA and related issues have been extensively investigated. Chapters 1–2 present the fundamental knowledge of VQA and ML. In Chap. 3, ML was exploited for image feature selection and image feature learning. Chapter 4 presents two ML-based frameworks for pooling image features of an image into a number score. In Chap. 5, two metric fusion frameworks designed to combine multiple existing metrics into a better one, were developed by the aid of ML tools.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Visual Quality Assessment by Machine LearningSummary and Remarks for Future Research

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Publisher
Springer Singapore
Copyright
© The Author(s) 2015
ISBN
978-981-287-467-2
Pages
123 –132
DOI
10.1007/978-981-287-468-9_6
Publisher site
See Chapter on Publisher Site

Abstract

[There has been increasing interest in visual quality assessment (VQA) during recent years. Of all these VQA methods, machine learning (ML) based ones became more and more popular. In this book, ML-based VQA and related issues have been extensively investigated. Chapters 1–2 present the fundamental knowledge of VQA and ML. In Chap. 3, ML was exploited for image feature selection and image feature learning. Chapter 4 presents two ML-based frameworks for pooling image features of an image into a number score. In Chap. 5, two metric fusion frameworks designed to combine multiple existing metrics into a better one, were developed by the aid of ML tools.]

Published: May 10, 2015

Keywords: Visual quality assessment; Visual attention; Just noticeable difference (JND); Computer graphics; Digital compound image; Joint audiovisual quality assessment

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